Cat x x
A⊂G A′ ⊂ M Oak x x
Potato x x
Formal concept analysis studies how objects can be grouped hierarchically based on their common attributes. It models concepts as units consisting of an extension (objects belonging to the concept) and an intension (attributes common to those objects). Formal contexts represent relationships between objects and attributes, and derivation operators identify the attributes common to a group of objects or the objects sharing a group of attributes.
Quantum artificial intelligence.
@ Kindly Follow my Instagram Page to discuss about your mental health problems-
-----> https://instagram.com/mentality_streak?utm_medium=copy_link
@ Appreciate my work:
-----> behance.net/burhanahmed1
Thank-you !
High Dimensional Data Visualization using t-SNEKai-Wen Zhao
Review of the t-SNE algorithm which helps visualizing the high dimensional data on manifold by projecting them onto 2D or 3D space with metric preserving.
Probability for Machine Learning
Here is a scant introduction to an important subject in Machine Learning. However, we are looking to work with our Probability Professor, Ofelia Begovich to write a series of notes in basic probability for something else, to improve this introduction. Nevertheless, there are still several things like:
1.- Linear Algebra
2.- Topology
3.- Mathematical Analysis
4.- Optimization
That need to be addressed, thus I am working in a class for intelligent systems for that.
Variational Autoencoders For Image GenerationJason Anderson
Meetup: https://www.meetup.com/Cognitive-Computing-Enthusiasts/events/260580395/
Video: https://www.youtube.com/watch?v=fnULFOyNZn8
Blog: http://www.compthree.com/blog/autoencoder/
Code: https://github.com/compthree/variational-autoencoder
An autoencoder is a machine learning algorithm that represents unlabeled high-dimensional data as points in a low-dimensional space. A variational autoencoder (VAE) is an autoencoder that represents unlabeled high-dimensional data as low-dimensional probability distributions. In addition to data compression, the randomness of the VAE algorithm gives it a second powerful feature: the ability to generate new data similar to its training data. For example, a VAE trained on images of faces can generate a compelling image of a new "fake" face. It can also map new features onto input data, such as glasses or a mustache onto the image of a face that initially lacks these features. In this talk, we will survey VAE model designs that use deep learning, and we will implement a basic VAE in TensorFlow. We will also demonstrate the encoding and generative capabilities of VAEs and discuss their industry applications.
From Data to Artificial Intelligence with the Machine Learning Canvas — ODSC ...Louis Dorard
The creation and deployment of predictive models that are at the core of artificially intelligent systems, is now being largely automated. However, formalizing the right machine learning problem that will leverage data to make applications and products more intelligent — and to create value — remains a challenge.
The Machine Learning Canvas is used by teams of managers, scientists and engineers to align their activities by providing a visual framework that helps specify the key aspects of AI systems: value proposition, data to learn from, usage of predictions, constraints, and measures of performance. In this presentation, we’ll motivate the usage of the MLC, we'll explain its structure, how to fill it in, and we’ll go over some example applications.
[딥논읽] Meta-Transfer Learning for Zero-Shot Super-Resolution paper reviewtaeseon ryu
105번째 논문리뷰,
오늘 소개 드릴 논문은 2020 CVPR에서 발표된 Meta-Transfer Learning for Zero-Shot Super-Resolution 라는 논문입니다!
제목에서 유추가 가능하신것 처럼 학습 데이터없이 저해상도 사진을 고해상도 사진으로 바꿔주는 Zero Shot Super Resolution을 위한 Meta Transfer Learning을 소개합니다. Internal Learning에 적합한 General한 초기 parameter를 찾는것에 기반하여 한번의 Gradient Update만으로 최적의 성능을 보여주는것 방법에 대해서 소개합니다.
논문에 대한 자세한 리뷰를 이미지 처리팀 김선옥 님이 자세한 리뷰 도와주셨습니다!
https://youtu.be/lEqbXLrUlW4
Part 1 of the Deep Learning Fundamentals Series, this session discusses the use cases and scenarios surrounding Deep Learning and AI; reviews the fundamentals of artificial neural networks (ANNs) and perceptrons; discuss the basics around optimization beginning with the cost function, gradient descent, and backpropagation; and activation functions (including Sigmoid, TanH, and ReLU). The demos included in these slides are running on Keras with TensorFlow backend on Databricks.
Quantum artificial intelligence.
@ Kindly Follow my Instagram Page to discuss about your mental health problems-
-----> https://instagram.com/mentality_streak?utm_medium=copy_link
@ Appreciate my work:
-----> behance.net/burhanahmed1
Thank-you !
High Dimensional Data Visualization using t-SNEKai-Wen Zhao
Review of the t-SNE algorithm which helps visualizing the high dimensional data on manifold by projecting them onto 2D or 3D space with metric preserving.
Probability for Machine Learning
Here is a scant introduction to an important subject in Machine Learning. However, we are looking to work with our Probability Professor, Ofelia Begovich to write a series of notes in basic probability for something else, to improve this introduction. Nevertheless, there are still several things like:
1.- Linear Algebra
2.- Topology
3.- Mathematical Analysis
4.- Optimization
That need to be addressed, thus I am working in a class for intelligent systems for that.
Variational Autoencoders For Image GenerationJason Anderson
Meetup: https://www.meetup.com/Cognitive-Computing-Enthusiasts/events/260580395/
Video: https://www.youtube.com/watch?v=fnULFOyNZn8
Blog: http://www.compthree.com/blog/autoencoder/
Code: https://github.com/compthree/variational-autoencoder
An autoencoder is a machine learning algorithm that represents unlabeled high-dimensional data as points in a low-dimensional space. A variational autoencoder (VAE) is an autoencoder that represents unlabeled high-dimensional data as low-dimensional probability distributions. In addition to data compression, the randomness of the VAE algorithm gives it a second powerful feature: the ability to generate new data similar to its training data. For example, a VAE trained on images of faces can generate a compelling image of a new "fake" face. It can also map new features onto input data, such as glasses or a mustache onto the image of a face that initially lacks these features. In this talk, we will survey VAE model designs that use deep learning, and we will implement a basic VAE in TensorFlow. We will also demonstrate the encoding and generative capabilities of VAEs and discuss their industry applications.
From Data to Artificial Intelligence with the Machine Learning Canvas — ODSC ...Louis Dorard
The creation and deployment of predictive models that are at the core of artificially intelligent systems, is now being largely automated. However, formalizing the right machine learning problem that will leverage data to make applications and products more intelligent — and to create value — remains a challenge.
The Machine Learning Canvas is used by teams of managers, scientists and engineers to align their activities by providing a visual framework that helps specify the key aspects of AI systems: value proposition, data to learn from, usage of predictions, constraints, and measures of performance. In this presentation, we’ll motivate the usage of the MLC, we'll explain its structure, how to fill it in, and we’ll go over some example applications.
[딥논읽] Meta-Transfer Learning for Zero-Shot Super-Resolution paper reviewtaeseon ryu
105번째 논문리뷰,
오늘 소개 드릴 논문은 2020 CVPR에서 발표된 Meta-Transfer Learning for Zero-Shot Super-Resolution 라는 논문입니다!
제목에서 유추가 가능하신것 처럼 학습 데이터없이 저해상도 사진을 고해상도 사진으로 바꿔주는 Zero Shot Super Resolution을 위한 Meta Transfer Learning을 소개합니다. Internal Learning에 적합한 General한 초기 parameter를 찾는것에 기반하여 한번의 Gradient Update만으로 최적의 성능을 보여주는것 방법에 대해서 소개합니다.
논문에 대한 자세한 리뷰를 이미지 처리팀 김선옥 님이 자세한 리뷰 도와주셨습니다!
https://youtu.be/lEqbXLrUlW4
Part 1 of the Deep Learning Fundamentals Series, this session discusses the use cases and scenarios surrounding Deep Learning and AI; reviews the fundamentals of artificial neural networks (ANNs) and perceptrons; discuss the basics around optimization beginning with the cost function, gradient descent, and backpropagation; and activation functions (including Sigmoid, TanH, and ReLU). The demos included in these slides are running on Keras with TensorFlow backend on Databricks.
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
Safalta Digital marketing institute in Noida, provide complete applications that encompass a huge range of virtual advertising and marketing additives, which includes search engine optimization, virtual communication advertising, pay-per-click on marketing, content material advertising, internet analytics, and greater. These university courses are designed for students who possess a comprehensive understanding of virtual marketing strategies and attributes.Safalta Digital Marketing Institute in Noida is a first choice for young individuals or students who are looking to start their careers in the field of digital advertising. The institute gives specialized courses designed and certification.
for beginners, providing thorough training in areas such as SEO, digital communication marketing, and PPC training in Noida. After finishing the program, students receive the certifications recognised by top different universitie, setting a strong foundation for a successful career in digital marketing.
Thinking of getting a dog? Be aware that breeds like Pit Bulls, Rottweilers, and German Shepherds can be loyal and dangerous. Proper training and socialization are crucial to preventing aggressive behaviors. Ensure safety by understanding their needs and always supervising interactions. Stay safe, and enjoy your furry friends!
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
1. Summer School
“Achievements and Applications of Contemporary Informatics,
Mathematics and Physics” (AACIMP 2011)
August 8-20, 2011, Kiev, Ukraine
̶ Formal Concept Analysis ̶
Erik Kropat
University of the Bundeswehr Munich
Institute for Theoretical Computer Science,
Mathematics and Operations Research
Neubiberg, Germany
2. Formal Concept Analysis
Formal Concept Analysis studies, how objects can be hierarchically grouped together
according to their common attributes.
Tree of Life
Source: Tree of Life Web Project
http://tolweb.org/tree/
4. What is a “concept” ?
A concept is a cognitive unit of meaning or a unit of knowledge.
Concept Bird
properties − feathered − warm-blooded
− winged − egg-laying
− bipedal − vertebrate
objects
blackbird, sparrow, raven,…
5. Formal Concept Analysis
• . . . is a powerful tool for data analysis, information retrieval,
and knowledge discovery in large databases.
• . . . is a conceptual clustering method,
which clusters simultaneously objects and their properties.
• . . . can mathematically represent, identify and analyze green yellow
conceptual structures.
red
2-dim
cylinder
disk
3-dim
triangle
cube
6. yellow
triangle
cube
green
Example disk
cylinder
red
3-dim 2-dim
3-dim
2-dim
yellow green red
7. Formal Concept Analysis
• . . . models concepts as units of thought, consisting of two parts:
− extension = objects belonging to the concept
− intension = attributes common to all those objects.
• . . . is an exploratory data analysis technique for discovering new knowledge.
• . . . can be used for efficiently computing association rules
applied in decision support systems.
• . . . can extract and visualize hierarchies !!!
8. Formal Concept Analysis
Goal: Derive automatically an ontology from a – very large – collection of objects
and their properties or features.
Target Marketing
Set of objects ⇒ clusters of objects
customers
correspond
⇔
one-for-one
Set of attributes
age, sex, income level, ⇒ clusters of attributes
spending habits, …
predict customer purchase decisions /
⇒ recommend products to customers
11. Example: Classification of plants and animals
Animal
Dog Cat
Plant
lives on land
Reed Water lily Oak
lives in water
Carp Potato
Objects Attributes
12. Formal Concept Analysis
Example: Classification of plants and animals
Attributes
Question:
Lives in water
Lives on land
Has object g the attribute m ( Yes / No ) ?
Animal
Plant
Dog x x
Cat x x
Oak x x
Binary Relation
Objects Potato x x
A formal context can be represented Carp x x
Water lily x x
by a cross table (bit-matrix).
Reed x x x
13. Formal Context
A formal context describes the relation between
objects and attributes.
A formal context (G, M, I) consists of
a set G of objects,
a set M of attributes and
a binary relation I ⊂ G x M.
Has object g the attribute m ( yes / no ) ?
14. Notation
• g I m means: “object g has attribute m”.
Example: (a) dog I animal
(b) carp I lives in water
16. The Derivation Operators (Type I)
A ⊂ G selection of objects.
Question: Which attributes from M are common to all these objects?
Lives in water
Set of common attributes of the objects in A
Lives on land
A’ := A↑:= { m ∈ M | g I m for all g ∈ A }
Animal
Plant
Dog x x
Cat x x
A⊂G A′ ⊂ M Oak x x
Potato x x
{Dog, Cat} Carp x x
{Oak, Potato} Water lily x x
Reed x x x
17. The Derivation Operators (Type I)
A ⊂ G selection of objects.
Question: Which attributes from M are common to all these objects?
Lives in water
Set of common attributes of the objects in A
Lives on land
A’ := A↑:= { m ∈ M | g I m for all g ∈ A }
Animal
Plant
Dog x x
Cat x x
A⊂G A′ ⊂ M Oak x x
Potato x x
{Dog, Cat} {Animal, lives on land} Carp x x
{Oak, Potato} Water lily x x
Reed x x x
18. The Derivation Operators (Type I)
A ⊂ G selection of objects.
Question: Which attributes from M are common to all these objects?
Lives in water
Set of common attributes of the objects in A
Lives on land
A’ := A↑:= { m ∈ M | g I m for all g ∈ A }
Animal
Plant
Dog x x
Cat x x
A⊂G A′ ⊂ M Oak x x
Potato x x
{Dog, Cat} {Animal, lives on land} Carp x x
{Oak, Potato} Water lily x x
Reed x x x
19. The Derivation Operators (Type I)
A ⊂ G selection of objects.
Question: Which attributes from M are common to all these objects?
Lives in water
Set of common attributes of the objects in A
Lives on land
A’ := A↑:= { m ∈ M | g I m for all g ∈ A }
Animal
Plant
Dog x x
Cat x x
A⊂G A′ ⊂ M Oak x x
Potato x x
{Dog, Cat} {Animal, lives on land} Carp x x
{Oak, Potato} {Plant, lives on land} Water lily x x
Reed x x x
20. The Derivation Operators (Type II)
B ⊂ M a set of attributes.
Question: Which objects have all the attributes from B?
Lives in water
Set of objects that have all the attributes from B
Lives on land
B’ := B↓:= { g ∈ G | g I m for all m ∈ B }
Animal
Plant
Dog x x
Cat x x
B⊂M B′ ⊂ G Oak x x
Potato x x
{Plant, lives on land} Carp x x
{Animal, lives in water} Water lily x x
Reed x x x
21. The Derivation Operators (Type II)
B ⊂ M a set of attributes.
Question: Which objects have all the attributes from B?
Lives in water
Set of objects that have all the attributes from B
Lives on land
B’ := B↓:= { g ∈ G | g I m for all m ∈ B }
Animal
Plant
Dog x x
Cat x x
B⊂M B′ ⊂ G Oak x x
Potato x x
{Plant, lives on land} {Oak, Potato, Reed} Carp x x
{Animal, lives in water} Water lily x x
Reed x x x
22. The Derivation Operators (Type II)
B ⊂ M a set of attributes.
Question: Which objects have all the attributes from B?
Lives in water
Set of objects that have all the attributes from B
Lives on land
B’ := B↓:= { g ∈ G | g I m for all m ∈ B }
Animal
Plant
Dog x x
Cat x x
B⊂M B′ ⊂ G Oak x x
Potato x x
{Plant, lives on land} {Oak, Potato, Reed} Carp x x
{Animal, lives in water} Water lily x x
Reed x x x
23. The Derivation Operators (Type II)
B ⊂ M a set of attributes.
Question: Which objects have all the attributes from B?
Lives in water
Set of objects that have all the attributes from B
Lives on land
B’ := B↓:= { g ∈ G | g I m for all m ∈ B }
Animal
Plant
Dog x x
Cat x x
B⊂M B′ ⊂ G Oak x x
Potato x x
{Plant, lives on land} {Oak, Potato, Reed} Carp x x
{Animal, lives in water} {Carp} Water lily x x
Reed x x x
24. 1) If a selection of objects is enlarged,
Derivation Operators - Facts then
the attributes which are common
Let (G, M, I) be a formal context. to all objects of the larger selection
are among
A, A1, A2 ⊂ G sets of objects.
the common attributes of the smaller selection.
B, B1, B2 ⊂ G sets of attributes.
1) A1 ⊂ A2 ⇒ A′2 ⊂ A′1 1′) B1 ⊂ B2 ⇒ B′2 ⊂ B′1
2) A ⊂ A′′ 2′) B ⊂ B′′
3) A′ = A′′′ 3′) B′ = B′′′
4) A ⊂ B′ ⇔ B ⊂ A′ ⇔ A x B ⊂ I
The derivation operators constitute a Galois connection
between the power sets P(G) and P (M).
26. Formal Concepts
Formal Context: Defines a relation between objects and attributes.
Real World: Objects are characterized by particular attributes.
Object
Attributes
27. Formal Concepts
Let (G, M, I) be a formal context, where A ⊂ G and B ⊂ M.
(A, B) is a formal concept of (G, M, I), iff
A′ = B and B′ = A.
The set A is called the extent and
the set B is called the intent
of the formal concept (A, B).
28. Formal Concepts
• Extent A and intent B of a formal concept (A,B)
correspond to each other by the binary relation I of the underlying formal context.
• The description of a formal concept is redundant,
because each of the two parts determines the other
Extent Intent
(objects) (attributes)
Duality
29. How can we find “formal concepts”?
Lives in water
Lives on land
A formal concept (A, B) corresponds to a
Animal
Plant
filled rectangular subtable
with row set A and column set B. Dog x x
Cat x x
Oak x x
Potato x x
( {Dog, Cat}, {Animal, lives on land} ) Carp x x
Water lily x x
Reed x x x
30. How can we find “formal concepts”?
Lives in water
Lives on land
A formal concept (A, B) corresponds to a
Animal
Plant
filled rectangular subtable
with row set A and column set B. Dog x x
Cat x x
Oak x x
Potato x x
( {Dog, Cat}, {Animal, lives on land} ) Carp x x
Water lily x x
Reed x x x
Each of the two parts determines the other!
31. Exercise
Determine the sets of objects A and the set of attributes B
such that the pair (A, B) represents a formal concept.
(a) A = {oak, potato, reed}, B = ?
(b) A = ?, B = {animal, lives in water}
32. How can we find “formal concepts”?
Lives in water
Lives on land
A formal concept (A, B) corresponds to a
Animal
Plant
filled rectangular subtable
with row set A and column set B. Dog x x
Cat x x
Oak x x
Potato x x
( {Dog, Cat}, {Animal, lives on land} ) Carp x x
Water lily x x
Reed x x
( {Oak, Potato, Reed}, {Plant, lives on land} ) x
33. How can we find “formal concepts”?
Lives in water
Lives on land
A formal concept (A, B) corresponds to a
Animal
Plant
filled rectangular subtable
with row set A and column set B. Dog x x
Cat x x
Oak x x
Potato x x
( {Dog, Cat}, {Animal, lives on land} ) Carp x x
Water lily x x
Reed x x
( {Oak, Potato, Reed}, {Plant, lives on land} ) x
( {Carp}, {Animal, lives in water} )
34. How can we find “formal concepts”?
Lives in water
Lives on land
A formal concept (A, B) corresponds to a
Animal
Plant
filled rectangular subtable
with row set A and column set B. Dog x x
Cat x x
Oak x x
Potato x x
Question: Is the following pair a formal concept? Carp x x
Water lily x x
Reed x x x
( {Oak, Potato}, {Plant, lives on land} )
35. How can we find “formal concepts”?
Lives in water
Lives on land
A formal concept (A, B) corresponds to a
Animal
Plant
filled rectangular subtable
with row set A and column set B. Dog x x
Cat x x
Oak x x
Potato x x
Question: Is the following pair a formal concept? Carp x x
Water lily x x
Reed x x x
( {Oak, Potato}, {Plant, lives on land} )
There exist filled rectangular subtables that do not determine formal concepts
36. Computing all Formal Concepts
Lemma
Each formal concept (A, B) of a formal context (G,M,I)
has the form (A′′, A′) for some subset A⊂G
and the form (B′, B′′) for some subset B ⊂ M.
Conversely, all such pairs are formal concepts.
Compute all formal concepts
37. Observations
• (A′′, A′) ist a formal concept.
• A ⊂ G extent ⇔ A = A′′.
B ⊂ M intent ⇔ B = B′′.
• The intersection of arbitrary many extents is an extent.
The intersection of arbitrary many intents is an intent.
38. Algorithm for Computing all Formal Concepts
A) Determine all Concept Extents
1. Initialize a list of concept extents.
Write for each attribute m ∈ M the extent {m}’ to the list.
2. For any two sets in the list, compute their intersection.
If the result is set that is not yet in the list, then extend the list by this set.
With the extended list, continue to build all pairwise intersections.
Extend the list by the set G.
⇒ The list contains all concept extents.
B) Determine all Concept Intents
3. Compute intents
For every concept extent A in the list compute the corresponding intent A′
to obtain a list of all formal concepts (A, A′).
40. Exercise
1. Initialize a list of concept extents.
Write for each attribute m ∈ M the extent {m}’ to the list.
Item Extent {m}' Attribute m∈M
e1 {Dog, Cat, Carp} {Animal}
e2 {Oak, Potato, Water lily, Reed} {Plant}
e3 {Dog, Cat, Oak, Potato, Reed} {Lives on land}
e4 {Carp, Water lily, Reed} {Lives in water}
41. Exercise
2. For any two sets in the list, compute their intersection.
- If the result is a set that is not yet in the list, then extend the list by this set.
- With the extended list, continue to build all pairwise intersections.
- Extend the list by the set G.
Item Extent Defined by
e1 {Dog, Cat, Carp} {Animal}
e2 {Oak, Potato, Water lily, Reed} {Plant}
e3 {Dog, Cat, Oak, Potato, Reed} {Lives on land}
e4 {Carp, Water lily, Reed} {Lives in water}
e5 ∅ e1 ∩ e2
e6 {Dog, Cat} e1 ∩ e3
e7 {Carp} e1 ∩ e4
e8 {Oak, Potato, Reed} e2 ∩ e3
e9 {Water lily, Reed} e2 ∩ e4
e10 {Reed} e3 ∩ e4
e11 {Dog, Cat, Oak, Potato, Carp, Water lily, Reed} G
42. Exercise
2. For any two sets in the list, compute their intersection.
- If the result is a set that is not yet in the list, then extend the list by this set.
- With the extended list, continue to build all pairwise intersections.
- Extend the list by the set G.
Item Extent Defined by
e1 {Dog, Cat, Carp} {Animal}
e2 {Oak, Potato, Water lily, Reed} {Plant}
e3 {Dog, Cat, Oak, Potato, Reed} {Lives on land}
e4 {Carp, Water lily, Reed} {Lives in water}
e5 ∅ e1 ∩ e2
e6 {Dog, Cat} e1 ∩ e3
e7 {Carp} e1 ∩ e4
e8 {Oak, Potato, Reed} e2 ∩ e3
e9 {Water lily, Reed} e2 ∩ e4
e10 {Reed} e3 ∩ e4
e11 {Dog, Cat, Oak, Potato, Carp, Water lily, Reed} G
43. Exercise
3. Determine intents
For every concept extent A in the list compute the corresponding intent A′
to obtain a list of all formal concepts (A, A′).
Item Extent A Intent A′
e1 {Dog, Cat, Carp} {Animal}
e2 {Oak, Potato, Water lily, Reed} {Plant}
e3 {Dog, Cat, Oak, Potato, Reed} {Lives on land}
e4 {Carp, Water lily, Reed} {Lives in water}
e5 ∅ M
e6 {Dog, Cat} {Animal, lives on land}
e7 {Carp} {Animal, lives in water}
e8 {Oak, Potato, Reed} {Plant, lives on land}
e9 {Water lily, Reed} {Plant, lives in water}
e10 {Reed} {Plant, lives on land, lives in water}
e11 {Dog, Cat, Oak, Potato, Carp, Water lily, Reed} ∅
45. Is there a relation between the formal concepts?
Animal super-concept
Dog, Cat, Carp
≤
Animal, lives on land Animal, lives in water
sub-concept
Dog, Cat Carp
Idea: Order concepts in a sub-concept ̶ super-concept hierarchy
46. Is there a relation between the formal concepts?
Animal super-concept
Dog, Cat, Carp
≤
Animal, lives on land Animal, lives in water
sub-concept
Dog, Cat Carp
The extent of the sub-concept is a subset of the extent of the super-concept
The intent of the super-concept is a subset of the intent of the sub-concept
47. Conceptual Hierarchy
Let (A1, B1) and (A2, B2) be formal concepts of (G,M,I).
(A1, B1) sub-concept of (A2, B2) :⇔ A1 ⊂ A2 [⇔ B2 ⊂ B1 ].
Animal
Dog, Cat, Carp
• (A2, B2) is a super-concept of (A1, B1).
• Notation: (A1, B1) ≤ (A2, B2)
Animal, lives on land
Dog, Cat
48. Conceptual Hierarchy
• The set of all formal concepts of (G, M, I)
is called the concept lattice of the formal context (G, M, I)
and is denoted by B (G,M,I) .
49. Conceptual Hierarchy
Theorem
The concept lattice of a formal context is a partially ordered set.
We need a notion of
neighborhood
⇒ We can draw figures that indicate intricate relationships!!
50. Conceptual Hierarchy
Let P be a set and ≤ is a binary relation on P.
A partially ordered set is a pair (P, ≤), iff
1) x≤x (reflexive)
2) x ≤ y and x ≠ y ⇒ ¬ y ≤ x (antisymmetric)
3) x ≤ y and y ≤ z ⇒ x ≤ z (transitive)
for all x, y, z ∈ P.
51. Conceptual Hierarchy
Let (A1, B1) and (A2, B2) be formal concepts of the context (G,M,I).
(A1, B1) proper sub-concept of (A2, B2) [ (A1, B1) < (A2, B2)]
:⇔ (A1, B1) ≤ (A2, B2) and (A1, B1) ≠ (A2, B2) .
(A2 , B2)
(A1 , B1)
52. Conceptual Hierarchy
Examples: In the following examples (A1, B1) is a proper sub-concept of (A2, B2)
(a) (A2 , B2) (b) (A2 , B2)
(A1 , B1) (A , B )
(A1 , B1)
Question: What is the difference between (a) and (b)?
Answer: In (a) the concept (A1, B1) is the lower neighbor of (A2, B2).
In (b) the concept (A1, B1) is not the lower neighbor of (A2, B2).
53. Conceptual Hierarchy
Proper sub-concepts can be used to define a notion of neighborhood.
Let (A1, B1) and (A2, B2) be formal concepts of the context (G,M,I) (A2 , B2)
and (A1, B1) is a proper sub-concept of (A2, B2).
(A1, B1) is a lower neighbor of (A2, B2) [(A1, B1) (A2, B2)], (A , B )
if no formal concept (A, B) exists with
(A1 , B1)
(A1, B1) < (A, B) < (A2, B2).
54. Drawing Concept Lattices
• Draw formal concepts
Draw a small circle for every formal concept.
A circle for a concept is always positioned higher than the circles of its proper sub-concepts.
• Draw lines
Connect each formal concept (circle) with the circles of its lower neighbors.
• Label with attribute names
Attach the attribute m to the circle representing the concept ( {m}′, {m}′′ ).
• Label with object names
Attach each object g to the circle representing the ({g}′′ , {g}′).
56. Drawing Concept Lattices
G
e11
plant e2 e4 aquatic e1 animal e3 terrestrial
water water
plant
e9 e7 animal e6 land
animal
terrestrial
e8 plants
water lily carp dog, cat oak, potato
plants, on land
e10
& in water
reed
e5
∅
57. Exercise
Compute the formal concepts of the following formal context:
Attributes
Habital zone
Terrestrial
Gas giant
Moon
Earth x x x
Jupiter x x
Objects
Mercury x
Mars x x
58. Exercise
1. Initialize a list of concept extents.
Write for each attribute m ∈ M the extent {m}’ to the list.
Item Extent {m}' Attribute m∈M
e1 {jupiter} {gas giant}
e2 {earth, mercury, mars} {terrestrial}
e3 {earth, jupiter, mars} {moon}
e4 {earth} {habital zone}
59. Exercise
2. For any two sets in the list, compute their intersection.
If the result is a set that is not yet in the list, then extend the list by this set.
With the extended list, continue to build all pairwise intersections.
Extend the list by the set G.
Item Extent Defined by
e1 {jupiter} {gas giant}
e2 {earth, mercury, mars} {terrestrial}
e3 {earth, jupiter, mars} {moon}
e4 {earth} {habital zone}
e5 ∅ e1 ∩ e2
e6 {earth, mars} e2 ∩ e3
e7 {earth, jupiter, mercury, mars} G
60. Exercise
3. Determine intents
For every concept extent A in the list compute the corresponding intent A′
to obtain a list of all formal concepts (A, A′).
Item Extent Intent
e1 {jupiter} {gas giant, moon}
e2 {earth, mercury, mars} {terrestrial}
e3 {earth, jupiter, mars} {moon}
e4 {earth} {terrestrial, moon, habital zone}
e5 ∅ M
e6 {earth, mars} {terrestrial, moon}
e7 {earth, jupiter, mercury, mars} ∅
61. Exercise
G
Concept Lattice e7
terrestrial moon
earth, mercury, e2 e3 earth, jupiter,
mars mars
terrestrial,
e6
moon
earth, mars
gas giant,
terrestrial,
e4 e1 moon
moon, habitual
jupiter
earth
e5
∅
63. Applications
• Web information retrieval
→ How can web search results retrieved by search engines be conceptualized
and represented in a human-oriented form.
• Partner selection for interfirm collaborations
→ Identification of structural similarities between potential partners
according to the characteristics of the prospective partner firms.
• Information systems for IT security management
→ Identification of security-sensitive operations performed by a server.
• Data warehousing and database analysis
→ Controlling the trade of stocks and shares.
66. Summary
• Formal concept analysis provides methods for an automatic derivation
of ontologies from very large collections of objects and their attributes.
• Reveal unknown, hidden and meaningful connections
between groups of objects and groups of attributes.
• The methods are supported by algebra, lattice theory and order theory.
• Visualization techniques are available.
• Strong connections to co-clustering (bi-clustering) methods
(important tools in DNA-microarray analysis).
67. Literature
• Bernhard Ganter, Gerd Stumme, Rudolf Wille (ed.)
Formal Concept Analysis. Foundations and Applications.
Springer, 2005.
• Claudio Carpineto, Giovanni Romano
Concept Data Analysis: Theory and Applications.
Wiley, 2004.
Software
www.fcahome.org.uk/fcasoftware.html